W3: Data Visualization

Data Visualization

Reminder: Penguins

gt::gt(head(penguins))

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Adelie	Torgersen	39.1	18.7	181	3750	male	2007
Adelie	Torgersen	39.5	17.4	186	3800	female	2007
Adelie	Torgersen	40.3	18.0	195	3250	female	2007
Adelie	Torgersen	NA	NA	NA	NA	NA	2007
Adelie	Torgersen	36.7	19.3	193	3450	female	2007
Adelie	Torgersen	39.3	20.6	190	3650	male	2007

bill_depth_mm : numeric
bill_length_mm : numeric
species : character

Common Plots

One Variable

Numeric: histogram
Character: bar plots

Two Variables

Numeric vs. Numeric: Scatterplot, line plot
Numeric vs. Character: Box plot

Why focus on these plots?

We build a plot one part at a time

Data +

Mapping to data +

Geometry

Think about making plots like using recipes from a cookbook: https://r-graphics.org/

Building a Histogram

ggplot(penguins) +

aes(x = bill_length_mm) +

geom_histogram()

Data +

Mapping to data +

Geometry

Taking it one part at a time (Data)

ggplot(penguins)

Taking it one part at a time (Aesthetics)

ggplot(penguins) +
  aes(x = bill_length_mm)

Taking it one part at a time (Geometry)

ggplot(penguins) +
  aes(x = bill_length_mm) +
  geom_histogram()

Histogram

ggplot(penguins) + aes(x = bill_length_mm) + geom_histogram()

Scatterplot

ggplot(penguins) +

aes(x = bill_length_mm, y = bill_depth_mm) +

geom_point()

Scatterplot (data)

ggplot(penguins)

Scatterplot (aesthetics)

ggplot(penguins) +
  aes(x = bill_length_mm, y=bill_depth_mm)

Scatterplot (geometry)

ggplot(penguins) +
  aes(x = bill_length_mm, y=bill_depth_mm) +
  geom_point()

What about more than two variables?

Three Variables

x=bill_length_mm
y=bill_depth_mm
color=species

ggplot(penguins) + aes(x = bill_length_mm, y = bill_depth_mm, color = species) + geom_point()

Bar plots

Made for categorical data. Bar plots automatically count each group for you, so you only need to provide one variable (axis).

ggplot(penguins) + aes(x = species) + geom_bar()

Bar plots, providing both axis

Alternatively, if you want to provide both axis for plotting:

penguins_grouped = group_by(penguins, species)
penguins_summary = summarise(penguins_grouped, n_species = n())
penguins_summary

# A tibble: 3 × 2
  species   n_species
  <fct>         <int>
1 Adelie          152
2 Chinstrap        68
3 Gentoo          124

ggplot(penguins_summary) + aes(x = species, y = n_species) + geom_bar(stat = "identity")

Histogram with a plot theme

ggplot(penguins) + aes(x = bill_length_mm) + geom_histogram() + theme_bw()

Histogram with options

ggplot(penguins) + aes(x = bill_length_mm) + geom_histogram(binwidth = 5)

Boxplot

ggplot(penguins) + aes(x = species, y = bill_depth_mm) + geom_boxplot()

Grouped Boxplot

ggplot(penguins) + aes(x = species, y = bill_depth_mm, color = island) + geom_boxplot()

Some additional options

ggplot(data = penguins) + aes(x = bill_length_mm, y = bill_depth_mm, color = species) + geom_point() + labs(x = “Bill Length”, y = “Bill Depth”, title = “Comparison of penguin bill length and bill depth across species”) + scale_x_continuous(limits = c(30, 60))

Summary of options

data

geom_point: x, y, color, shape

geom_line: x, y, group, color

geom_histogram: x, y, fill

geom_bar: x, fill

geom_boxplot: x, y, fill, color

facet_wrap

labs

scale_x_continuous

scale_y_continuous

scale_x_discrete

scale_y_discrete

`esquisse` as a helper

Consider the esquisse package to help generate your ggplot code via drag and drop.

library(esquisse)

esquisser(penguins)

R Graphics Cookbook

An excellent resource: https://r-graphics.org/